Question

In the Gradient Descent algorithm, we are more likely to reach the global minimum, if the learning rate is selected to be a large value.

a. True
b. False

Answer 1

False iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii

Answer 2

Answer:

false i think.

Explanation:

Gradient Descent is more likely to reach a local minima. because starting at different points and just in general having a different starting point, will lead us to a different local minimum( aka the lowest point closest to the starting point). if alpha(the learning rate) is too large, gradient descent may fail to converge and may even diverge.